Superiority of 18F-FAPI-42 PET/CT in the detection of primary tumor and management of appendiceal neoplasm to 18F-FDG PET/CT and CE-CT.

BACKGROUND: In the present study, we investigated the value of 18F-fibroblast-activation protein inhibitor (FAPI) positron emission tomography/computed tomography (18F-FAPI-42 PET/CT) to preoperative evaluations of appendiceal neoplasms and management for patients. METHODS: This single-center retrospective clinical study, including 16 untreated and 6 treated patients, was performed from January 2022 to May 2023 at Southern Medical University Nanfang Hospital. Histopathologic examination and imaging follow-up served as the reference standard. 18F-FAPI-42 PET/CT was compared to 18F-fluorodeoxyglucose (18F-FDG) PET/CT and contrast-enhanced CT (CE-CT) in terms of maximal standardized uptake value (SUVmax), diagnostic efficacy and impact on treatment decisions. RESULTS: The accurate detection of primary tumors and peritoneal metastases were improved from 28.6% (4/14) and 50% (8/16) for CE-CT, and 43.8% (7/16) and 85.0% (17/20) for 18F-FDG PET/CT, to 87.5% (14/16) and 100% (20/20) for 18F-FAPI-42 PET/CT. Compared to 18F-FDG PET/CT, 18F-FAPI-42 PET/CT detected more regions infiltrated by peritoneal metastases (108 vs. 43), thus produced a higher peritoneal cancer index (PCI) score (median PCI: 12 vs. 5, P < 0.01). 18F-FAPI-42 PET/CT changed the intended treatment plans in 35.7% (5/14) of patients compared to CE-CT and 25% (4/16) of patients compared to 18F-FDG PET/CT but did not improve the management of patients with recurrent tumors. CONCLUSIONS: The present study revealed that 18F-FAPI-42 PET/CT can supplement CE-CT and 18F-FDG PET/CT to provide a more accurate detection of appendiceal neoplasms and improved treatment decision making for patients.

Predicting balloon shape in percutaneous microcompression : an observational comparative analysis of Meckel's cave imaging and balloon morphology.

Achieving a pear-shaped balloon holds pivotal significance in the context of successful percutaneous microcompression procedures for trigeminal neuralgia. However, inflated balloons may assume various configurations, whether it is inserted into Meckel's cave or not. The absence of an objective evaluation metric has become apparent. To investigate the relationship between the morphology of Meckel's Cave and the balloon used in percutaneous microcompression for trigeminal neuralgia and establish objective criteria for assessing balloon shape in percutaneous microcompression procedures. This retrospective study included 58 consecutive patients with primary trigeminal neuralgia. Data included demographic, clinical outcomes, and morphological features of Meckel's cave and the balloon obtained from MRI and Dyna-CT imaging. MRI of Meckel's cave and Dyna-CT of intraoperative balloon were modeled, and the morphological characteristics and correlation were analyzed. The reconstructed balloon presented a fuller morphology expanding outward and upward on the basis of Meckel's cave. The projected area of balloon was strongly positively correlated with the projected area of Meckel's cave. The Pearson correlation coefficients were 0.812 (P<0.001) for axial view, 0.898 (P<0.001) for sagittal view and 0.813 (P<0.001) for coronal view. Similarity analysis showed that the sagittal projection image of Meckel's cave and that of the balloon had good similarity. This study reveals that the balloon in percutaneous microcompression essentially represents an expanded morphology of Meckel's cave, extending outward and upward. There is a strong positive correlation between the volume and projected area of the balloon and that of Meckel's cave. Notably, the sagittal projection image of Meckel's cave serves as a reliable predictor of the intraoperative balloon shape. This method has a certain generalizability and can help providing objective criteria for judging balloon shape during percutaneous microcompression procedures.

Radiologic-Pathologic Correlation of Cardiac Tumors: Updated 2021 WHO Tumor Classification.

Cardiac tumors, although rare, carry high morbidity and mortality rates. They are commonly first identified either at echocardiography or incidentally at thoracoabdominal CT performed for noncardiac indications. Multimodality imaging often helps to determine the cause of these masses. Cardiac tumors comprise a distinct category in the World Health Organization (WHO) classification of tumors. The updated 2021 WHO classification of tumors of the heart incorporates new entities and reclassifies others. In the new classification system, papillary fibroelastoma is recognized as the most common primary cardiac neoplasm. Pseudotumors including thrombi and anatomic variants (eg, crista terminalis, accessory papillary muscles, or coumadin ridge) are the most common intracardiac masses identified at imaging. Cardiac metastases are substantially more common than primary cardiac tumors. Although echocardiography is usually the first examination, cardiac MRI is the modality of choice for the identification and characterization of cardiac masses. Cardiac CT serves as an alternative in patients who cannot tolerate MRI. PET performed with CT or MRI enables metabolic characterization of malignant cardiac masses. Imaging individualized to a particular tumor type and location is crucial for treatment planning. Tumor terminology changes as our understanding of tumor biology and behavior evolves. Familiarity with the updated classification system is important as a guide to radiologic investigation and medical or surgical management. ©RSNA, 2024 Supplemental material is available for this article.

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries.

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Accurate object localization facilitates automatic esophagus segmentation in deep learning.

BACKGROUND: Currently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task. METHODS: A total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized. RESULTS: The mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions. CONCLUSION: The 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Updated 2022 ACC/AHA Guideline Improves Concordance Between TTE and CT in Monitoring Marfan Snydrome and Related Disorders, but Relevant Measurement Differences Remain Frequent.

Background: Patients diagnosed with Marfan syndrome or a related syndrome require frequent aorta monitoring using imaging techniques like transthoracic echocardiography (TTE) and computed tomography (CT). Accurate aortic measurement is crucial, as even slight enlargement (>2 mm) often necessitates surgical intervention. The 2022 ACC/AHA guideline for Aortic Disease Diagnosis and Management includes updated imaging recommendations. We aimed to compare these with the 2010 guideline. Methods: This retrospective study involved 137 patients with Marfan syndrome or a related disorder, undergoing TTE and ECG-triggered CT. Aortic diameter measurements were taken based on the old 2010 guideline (TTE: inner edge to inner edge, CT: external diameter) and the new 2022 guideline (TTE: leading edge to leading edge, CT: internal diameter). Bland-Altman plots compared measurement differences. Results: Using the 2022 guideline significantly reduced differences outside the clinical agreement limit from 49% to 26% for the aortic sinus and from 41% to 29% for the ascending aorta. Mean differences were -0.30 mm for the aortic sinus and +1.12 mm for the ascending aorta using the 2022 guideline, compared to -2.66 mm and +1.21 mm using the 2010 guideline. Conclusion: This study demonstrates for the first time that the 2022 ACC/AHA guideline improves concordance between ECG-triggered CT and TTE measurements in Marfan syndrome patients, crucial for preventing life-threatening aortic complications. However, the frequency of differences >2 mm remains high. Clinical Relevance/Application: Accurate aortic diameter measurement is vital for patients at risk of fatal aortic complications. While the 2022 guideline enhances concordance between imaging modalities, frequent differences >2 mm persist, potentially impacting decisions on aortic repair. The risk of repeat radiation exposure from ECG-triggered CT, considered the 'gold standard', continues to be justified.

Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc., Kyoto, Japan) containing a set of 3D-printed lung nodules including six diameters (4 to 9 mm) and three morphology classes (lobular, spiculated, smooth), with an established ground truth. Images were acquired at varying radiation doses (6.04, 3.03, 1.54, 0.77, 0.41 and 0.20 mGy) and reconstructed with combinations of reconstruction kernels (soft and hard kernel) and reconstruction algorithms (ASIR-V and DLIR at low, medium and high strength). Semi-automatic volumetry measurements and subjective image quality scores recorded by five radiologists were analyzed with multiple linear regression and mixed-effect ordinal logistic regression models. RESULTS: Volumetric errors of nodules imaged with DLIR are up to 50% lower compared to ASIR-V, especially at radiation doses below 1 mGy and when reconstructed with a hard kernel. Also, across all nodule diameters and morphologies, volumetric errors are commonly lower with DLIR. Furthermore, DLIR renders higher subjective IQ, especially at the sub-mGy doses. Radiologists were up to nine times more likely to score the highest IQ-score to these images compared to those reconstructed with ASIR-V. Lung nodules with irregular margins and small diameters also had an increased likelihood (up to five times more likely) to be ascribed the best IQ scores when reconstructed with DLIR. CONCLUSION: We observed that DLIR performs as good as or even outperforms conventionally used reconstruction algorithms in terms of volumetric accuracy and subjective IQ of nodules in an anthropomorphic chest phantom. As such, DLIR potentially allows to lower the radiation dose to participants of lung cancer screening without compromising accurate measurement and characterization of lung nodules.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Assessment of myosteatosis and functionality in pretreatment gynecological cancer patients.

PURPOSE: We aimed to investigate the relationship between pretreatment gynecologic cancer survival and the physical function of patients with myosteatosis. Understanding this relationship prior to treatment would help healthcare providers identify and refer patients with poor muscle quality to an exercise program prior to treatment. METHODS: We conducted a cross-sectional analysis of 73 GC patients. Physical function was quantified using handgrip strength and an adapted version of the Senior Fitness Test (aerobic endurance not included). The EORTC QLC-C30 was used to evaluate general health quality. Myosteatosis (values below the median muscle radiodensity), muscle mass, and adipose tissue variables were calculated from the computed tomography (CT) scan at the third lumbar vertebra using specific software. RESULTS: Seventy patients (50.9 ± 15.2) were included; 41.5% had stage III or IV disease, and 61.4% had cervical cancer. The myosteatosis group was 11.9 years older and showed reduced functioning compared to the normal-radiodensity group. Age and Timed Up and Go (TUG) test results were shown to be the most reliable predictors of muscle radiodensity in pretreatment gynecological patients according to multivariate regression analysis (R2 = 0.314). CONCLUSION: Gynecological healthcare professionals should be aware that prompt exercise programs might be especially beneficial for older patients with reduced TUG performance to preserve muscle function and quality.

[VASCULAR OPHTHALMIC MANIFESTATIONS IN COVID-19 PATIENTS].

INTRODUCTION: Although COVID-19 is mainly a respiratory disease, recent evidence has emerged of vascular and procoagulant pathologies even in young and otherwise healthy individuals. Ophthalmic manifestations include, among others, visual impairment due to arteritic and venous retinal obstructions, which at times precedes other aspects of the disease. We present two atypical cases of internal carotid dissection (ICAD) and review the different ocular symptoms of ICAD and its association with the COVID-19 pandemic. BACKGROUND: A 43-year-old otherwise healthy man was referred to the Emergency Department with a headache and monocular blurring of vision. A recent fever (2 weeks prior) was noted on anamnesis, in light of absence of available positive PCR test during the illness period, clinical suspicion of COVID-19 was assumed. An initial ophthalmic evaluation found a mild optic nerve function impairment with preserved visual acuity. Computed tomography (CT) showed sinusitis, and an initial diagnosis was made of mild optic neuropathy secondary to sphenoid sinusitis. A few hours after admission, the patient reported deterioration of symptoms and examination revealed no light perception in his right eye and pale edematous optic nerve. Urgent magnetic resonance angiography (MRA) demonstrated right ICAD with no additional findings. The second patient, a 43-year-old man developed an acute event of strabismus, left limb paralysis, and speech difficulties while on a hospital visit for his son. The patient underwent CT of the brain which demonstrated extensive infarction following the distribution of his right cerebral artery. Continued investigation using computed tomography angiography (CTA) demonstrated a dissection of the right internal carotid artery. The patient was positive for COVID-19. DISCUSSION: In this review, we discuss 2 cases of carotid artery dissection presenting with an acute ocular complaint in two otherwise healthy young individuals. Events were suspected to have been provoked by COVID-19 infection. The pathogenesis and mechanisms behind COVID-19 induced coagulopathy are not clear, and several mechanisms have been proposed including endothelial damage and dysfunction. The virus is thought to enter endothelial cells and lead to a pathological procoagulant state. Awareness should be drawn to uncommon signs especially in young adults. Clotting issues can arise and should be treated quickly as they might be life and vision threatening.

Developing and validating multi-omics prediction models for late patient-reported dysphagia in head and neck radiotherapy.

Background and purpose. To investigate models developed using radiomic and dosiomic (multi-omics) features from planning and treatment imaging for late patient-reported dysphagia in head and neck radiotherapy.Materials and methods. Training (n = 64) and testing (n = 23) cohorts of head and neck cancer patients treated with curative intent chemo-radiotherapy with a follow-up time greater than 12 months were retrospectively examined. Patients completed the MD Anderson Dysphagia Inventory and a composite score ≤60 was interpreted as patient-reported dysphagia. A chart review collected baseline dysphagia and clinical factors. Multi-omic features were extracted from planning and last synthetic CT images using the pharyngeal constrictor muscle contours as a region of interest. Late patient-reported dysphagia models were developed using a random forest backbone, with feature selection and up-sampling methods to account for the imbalanced data. Models were developed and validated for multi-omic feature combinations for both timepoints.Results. A clinical and radiomic feature model developed using the planning CT achieved good performance (validation: sensitivity = 80 ± 27% / balanced accuracy = 71 ± 23%, testing: sensitivity = 80 ± 10% / balanced accuracy = 73 ± 11%). The synthetic CT models did not show improvement over the plan CT multi-omics models, with poor reliability of the radiomic features on these images. Dosiomic features extracted from the synthetic CT showed promise in predicting late patient-reported dysphagia.Conclusion. Multi-omics models can predict late patient-reported dysphagia in head and neck radiotherapy patients. Synthetic CT dosiomic features show promise in developing successful models to account for changes in delivered dose distribution. Multi-center or prospective studies are required prior to clinical implementation of these models.

Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning.

BACKGROUND: Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models. METHODS: Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients. RESULTS: The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively. CONCLUSIONS: The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.

[Comparative evaluation of the accuracy of 3D TMJ analysis performed by different methods of processing computed tomograms]. / Sravnitel'naya otsenka tochnosti 3D-analiza elementov visochno-nizhnechelyustnogo sustava, vypolnennogo raznymi sposobami obrabotki komp'yuternykh tomogramm.

OBJECTIVE: The aim of this study. Comparison of the accuracy of segmentation of TMJ elements in different ways and assessment of the suitability of the data obtained for the diagnosis of TMJ dysfunction. MATERIALS AND METHODS: To study the segmentation of the bone elements of the TMJ (articular fossa, head of the LF), 60 computed tomograms of the maxillofacial region of patients were randomly selected in various ways (archival material). In group 1, the results of CT processing by AI diagnostics algorithms (Russia) were collected; in group 2, the results of CT processing based on the semi-automatic segmentation method in the Avantis3D program. The results of CT processing by Avantis3D AI algorithms (Russia) with different probability modes - 0.4 and 0.9, respectively, were selected for the third and fourth groups. Visually, the coincidence of the contours of the LF heads and articular pits isolated using different methods with their contours on all possible sections of the original CT itself was evaluated. The time spent on TMJ segmentation according to CT data was determined and compared using the methods described above. RESULTS: Of the 240 objects, only 7.5% of the cases showed a slight discrepancy between the contours of the original CT in group b1, which was the lowest of all. A slight discrepancy in the TMJ contours to be corrected is characteristic of the semi-automatic method of segmentation by optical density was detected in 50.4% (group 2). The largest percentage of significant errors not subject to correction was noted in the first group, which made it impossible to perform a full 3D analysis of the TMJ, and the smallest in the second and fourth. The magnitude of the error in determining the width of the articular gap in different groups is comparable to the size of one voxel per CT. When segmentation is carried out using AI, the difference between segmented objects is close to zero values. The average time spent on TMJ segmentation in group 1 was 10.2±1.23 seconds, in group 2 - 12.6±1.87 seconds, in groups 3 and 4 - 0.46±0.12 seconds and 0.46±0.13 seconds, respectively. CONCLUSION: The developed automated method for segmenting TMJ elements using AI is obviously more suitable for practical work, since it requires minimal time, and is almost as accurate as other methods under consideration.

Acute muscle loss assessed using panoramic ultrasound in critically ill adults: a prospective observational study.

PURPOSE: Panoramic ultrasound is one of the recently introduced ultrasound evaluation techniques. We herein examined the relationship between the cross-sectional area of the rectus femoris muscle on panoramic ultrasound and its volume based on the gold standard computed tomography (CT) evaluation. METHODS: This was a single-center prospective observational study. A panoramic ultrasound assessment of the cross-sectional area of the rectus femoris muscle and a simple CT evaluation of its volume were performed on days 1 and 7 of hospitalization. Physical functions were assessed at discharge. RESULTS: Twenty patients were examined. The rate of change in the cross-sectional area of the rectus femoris muscle on panoramic ultrasound correlated with that in its volume on CT (correlation coefficient 0.59, p = 0.0061). In addition, a correlation was observed between the absolute value for the rectus femoris muscle cross-sectional area on panoramic ultrasound and physical functions at discharge. Rectus femoris muscle distances did not correlate with either. CONCLUSION: In the acute phase of critical illness, the cross-sectional area of the rectus femoris muscle on panoramic images correlated with its volume on CT and, thus, it is a valid method for assessing muscle mass.

Normative contact mechanics of the ankle Joint: Quantitative assessment utilizing bilateral weightbearing CT.

Alterations in ankle's articular contact mechanics serve as one of the fundamental causes of significant pathology. Nevertheless, computationally intensive algorithms and lack of bilateral weightbearing imaging have rendered it difficult to investigate the normative articular contact stress and side-to-side differences. The aims of our study were two-fold: 1) to determine and quantify the presence of side-to-side contact differences in healthy ankles and 2) to establish normative ranges for articular ankle contact parameters. In this retrospective comparative study, 50 subjects with healthy ankles on bilateral weight-bearing CT were confirmed eligible. Segmentation into 3D bony models was performed semi-automatically, and individualized cartilage layers were modelled based on a previously validated methodology. Contact mechanics were evaluated by using the mean and maximum contact stress of the tibiotalar articulation. Absolute and percentage reference range values were determined for the side-to-side difference. Amongst a cohort of individuals devoid of ankle pathology, mean side-to-side variation in these measurements was < 12 %, while respective differences of > 17 % talar peak stress and > 31 % talar mean stress indicate abnormality. No significant differences were found between laterality in any of the evaluated contact parameters. Understanding these values may promote a more accurate assessment of ankle joint biomechanics when distinguishing acceptable versus pathological contact mechanics in clinical practice.

Thyroid abnormalities identified on CT screening for lung cancer.

PURPOSE: Because incidental thyroid nodules (ITNs) are common extrapulmonary findings in low-dose computed tomography (LDCT) scans for lung cancer screening, we aimed to investigate the frequency of ITNs on LDCT scans separately on baseline and annual repeat scans, the frequency of malignancy among the ITNs, and any association with demographic, clinical, CT characteristics. METHODS: Retrospective case series of all 2309 participants having baseline and annual repeat screening in an Early Lung and Cardiac Action Program (MS-ELCAP) LDCT lung screening program from January 2010 to December 2016 was performed. Frequency of ITNs in baseline and annual repeat rounds were determined. Multivariable regression analysis was performed to identify significant predictors. RESULTS: Dominant ITNs were seen in 2.5 % of 2309 participants on baseline and in 0.15 % of participants among 4792 annual repeat LDCTs. The low incidence of new ITNs suggests slow growth as it would take approximately an average of 16.8 years for a new ITN to be detected on annual rounds of screening. Newly detected ITNs on annual repeat LDCT were all smaller than 15 mm. Regression analysis showed that the increasing of age, coronary artery calcifications score and breast density grade were significant predictors for females having an ITN. No significant predictors were found for ITNs in males. CONCLUSION: ITNs are detected at LDCT however, no malignancy was found. Certain predictors for ITNs in females have been identified including breast density, which may point towards a common causal pathway.

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.

Breast arterial calcification is associated with sarcopenia in peri- and post-menopausal women.

PURPOSE: Breast arterial calcification (BAC) refers to medial calcium deposition in breast arteries and is detectable via mammography. Sarcopenia, which is characterised by low skeletal muscle mass and quality, is associated with several serious clinical conditions, increased morbidity, and mortality. Both BAC and sarcopenia share common pathologic pathways, including ageing, diabetes, and chronic kidney disease. Therefore, this study evaluated the relationship between BAC and sarcopenia as a potential indicator of sarcopenia. METHODS: This study involved women aged >40. BAC was evaluated using digital mammography and was defined as vascular calcification. Sarcopenia was assessed using abdominal computed tomography. The cross-sectional skeletal mass area was measured at the third lumbar vertebra level. The skeletal mass index was obtained by dividing the skeletal mass area by height in square meters(m2). Sarcopenia was defined as a skeletal mass index of ≤38.5 cm2/m2. A multivariable model was used to evaluate the relationship between BAC and sarcopenia. RESULTS: The study involved 240 participants. Of these, 36 (15 %) were patients with BAC and 204 (85 %) were without BAC. Sarcopenia was significantly higher among the patients with BAC than in those without BAC (72.2 % vs 17.2 %, P < 0.001). The multivariable model revealed that BAC and age were independently associated with sarcopenia (odds ratio[OR]: 7.719, 95 % confidence interval[CI]: 3.201-18.614, and P < 0.001 for BAC and OR: 1.039, 95 % CI: 1.007-1.073, P = 0.01 for age). CONCLUSION: BAC is independently associated with sarcopenia. BAC might be used as an indicator of sarcopenia on screening mammography.

Epicardial adipose tissue volume assessed by cardiac CT as a predictor of atrial fibrillation recurrence following catheter ablation.

INTRODUCTION: In patients with atrial fibrillation (AF), up to one third have recurrence after a first catheter ablation (CA). Epicardial adipose tissue (EAT) has been considered to be closely related to AF, with a potential role in its recurrence. We aimed to evaluate the association between the volume of EAT measured by cardiac computed tomography (CT) and AF recurrence after CA. METHODS: Consecutive AF patients underwent a standardized cardiac CT protocol for quantification of EAT, thoracic adipose volume (TAV) and left atrium (LA) volume before CA. An appropriate cut-off of EAT was determined and risk recurrence was estimated. RESULTS: 305 patients (63.6 % male, mean age 57.5 years, 28.2 % persistent AF) were followed for 24 months; 23 % had AF recurrence at 2-year mark, which was associated with higher EAT (p = 0.037) and LAV (p < 0.001). Persistent AF was associated with higher EAT volumes (p = 0.010), TAV (p = 0.003) and LA volumes (p < 0.001). EAT was predictive of AF recurrence (p = 0.044). After determining a cut-off of 92 cm3, survival analysis revealed that EAT volumes > 92 cm3 showed higher recurrence rates at earlier time points after the index ablation procedure (p = 0.006), with a HR of 1.95 (p = 0.008) of AF recurrence at 2-year. After multivariate adjustment, EAT > 92 cm3 remained predictive of AF recurrence (p = 0.028). CONCLUSION: The volume of EAT measured by cardiac CT can predict recurrence of AF after ablation, with a volume above 92 cm3 yielding almost twice the risk of arrhythmia recurrence in the first two years following CA. Higher EAT and TAV are also associated with persistent AF.

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.